REPOGEO REPORT · LITE

sail-sg/understand-r1-zero

Default branch main · commit dfca49dd · scanned 7/1/2026, 11:12:41 AM

GitHub: 1,264 stars · 60 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface sail-sg/understand-r1-zero, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Add a concise introductory sentence to the README to clarify the repository's purpose.

Why:

COPY-PASTE FIX

Add the following sentence immediately after the main title in the README:

This repository provides the official codebase and resources for our paper, 'Understanding R1-Zero-Like Training: A Critical Perspective,' offering a critical analysis and empirical investigation into R1-Zero-like training for large language model reasoning.

mediumabout#2

Enhance the repository description for better clarity and categorization.

Why:

CURRENT

Understanding R1-Zero-Like Training: A Critical Perspective

COPY-PASTE FIX

Official codebase for 'Understanding R1-Zero-Like Training: A Critical Perspective,' providing empirical analysis and insights into R1-Zero-like training for LLM reasoning. Includes models and code.

mediumtopics#3

Add more specific topics to improve AI categorization.

Why:

CURRENT

llm, r1-zero, reasoning, rl

COPY-PASTE FIX

llm, r1-zero, reasoning, rl, critical-analysis, empirical-study, research-codebase, reinforcement-learning-theory

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface sail-sg/understand-r1-zero

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/trl

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

huggingface/trl · recommended 1×
deepmind/acme · recommended 1×
openai/baselines · recommended 1×
openai/spinningup · recommended 1×
langchain-ai/langchain · recommended 1×

CATEGORY QUERY
How to evaluate and improve large language model reasoning capabilities using reinforcement learning?
you: not recommended
AI recommended (in order):
1. Hugging Face TRL (huggingface/trl)
2. DeepMind's Acme (deepmind/acme)
3. OpenAI's Baselines (openai/baselines)
4. OpenAI's Spinning Up (openai/spinningup)
5. LangChain (langchain-ai/langchain)
6. LlamaIndex (run-llama/llama_index)
AI recommended 6 alternatives but never named sail-sg/understand-r1-zero. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking resources to critically analyze advanced reinforcement learning techniques for LLM reasoning.
you: not recommended
AI recommended (in order):
1. AlphaCode 2
2. InstructGPT
3. Claude
4. Constitutional AI
5. Tree of Thoughts (ToT)
6. Graph of Thoughts (GoT)
7. WebGPT
8. Toolformer
AI recommended 8 alternatives but never named sail-sg/understand-r1-zero. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of sail-sg/understand-r1-zero?
pass
AI named sail-sg/understand-r1-zero explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts sail-sg/understand-r1-zero in production, what risks or prerequisites should they evaluate first?
pass
AI named sail-sg/understand-r1-zero explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo sail-sg/understand-r1-zero solve, and who is the primary audience?
pass
AI did not name sail-sg/understand-r1-zero — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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sail-sg/understand-r1-zero — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite